Biocontrol Traits Correlate With Resistance to Predation by Protists in Soil Pseudomonads

Abstract

Root-colonizing bacteria can support plant growth and help fend off pathogens. It is clear that such bacteria benefit from plant-derived carbon, but it remains ambiguous why they invest in plant-beneficial traits. We suggest that selection via protist predation contributes to recruitment of plant-beneficial traits in rhizosphere bacteria. To this end, we examined the extent to which bacterial traits associated with pathogen inhibition coincide with resistance to protist predation. We investigated the resistance to predation of a collection of Pseudomonas spp. against a range of representative soil protists covering three eukaryotic supergroups. We then examined whether patterns of resistance to predation could be explained by functional traits related to plant growth promotion, disease suppression and root colonization success. We observed a strong correlation between resistance to predation and phytopathogen inhibition. In addition, our analysis highlighted an important contribution of lytic enzymes and motility traits to resist predation by protists. We conclude that the widespread occurrence of plant-protective traits in the rhizosphere microbiome may be driven by the evolutionary pressure for resistance against predation by protists. Protists may therefore act as microbiome regulators promoting native bacteria involved in plant protection against diseases.

Keywords: PGPR; biocontrol; multitrophic interactions; protozoa; rhizobacteria.

FIGURE 1

Active protist densities grown on different bacterial isolates (No added cells, E. coli OP50, and Pseudomonas spp.) at day 3, in 2% KB, shown for all protists together. Asterisks indicate significant differences compared to the control (protist grown on the E. coli OP50) reported from the binomial regression part of the hurdle model (see also Supplementary Table S5).

FIGURE 2

Active protist densities grown on each bacterial strain at day 3, in 2% KB, shown for individual predator-prey co-cultures. The different colors of the heatmap represent the normalized protist density on each bacterial isolate. White (corresponding to a value of 0) indicates the average density per protist isolate (per row). Orange indicates lower density compared to the average of a protist isolate and blue indicates higher density compared to the average of a protist isolate. Asterisks indicate level of significance in protist density grown on the given bacterial isolate relative to growth with E. coli OP50. The protist isolate and bacterial isolates are displayed based on their phylogenetic relatedness. Phylogenetic trees are based on the Maximum-Likelihood Method using the concatenated partial sequences from 16S rRNA, rpoB, and oprF genes for the pseudomonads and the 18S rRNA gene for the protist isolates.

FIGURE 3

Bacterial density (OD₆₀₀) plotted against protist density at day 3, in 2% KB. Each point represents the mean of 5 replicates.

FIGURE 4

Correlation matrix between protist growth and selected prey bacterial traits. Red and blue dots indicate negative and positive correlations, respectively. Dashed circles are statistically significant. The different bacterial traits are grouped together according to the indexes: BPI, Biocontrol Potential Index; DGPI, Direct Plant Growth Promotion; CPI, Colonization Potential Index.

FIGURE 5

Spearman rank correlation between the combined protist density and the combined bacterial density with the different bacterial indices calculated by Agaras et al. (2015). Red and blue dots indicate negative and positive correlations, respectively. Dashed circles are statistically significant. Biocontrol Potential Index (BPI; e.g., antibiotic genes, HCN production, lytic enzymes), Direct Growth Promotion Index (DGPI; e.g., P solubilization, IAA, ACC deaminase), and Colonization Potential Index (CPI; motility, quorum sensing, biofilm).